Risk of cardiovascular disease among teachers in Cape Town: Findings of the South African PaCT pilot study
Bibliographic record
Abstract
BACKGROUND: The accelerating epidemic of cardiovascular disease (CVD) and other non-communicable diseases (NCDs) highlights the need to establish long-term cohort studies in Africa. OBJECTIVE: The Partnership for Cohort Research and Training (PaCT) seeks to study NCDs in South Africa (SA), Uganda, Tanzania and Nigeria on a long-term basis. Pilot studies at each site have tested feasibility. The SA site additionally studied the prevalence of CVD risk factors and categorised participants' 10-year predicted risk of a cardiovascular event. METHODS: We enrolled teachers from 111 public schools in the Metro South Education District in Cape Town, SA, between January 2011 and May 2012. Participants completed a self-administered questionnaire and biological measurements, and chose post or email for 6-month follow-up. RESULTS: The participation of schools was permitted by 53.2% of principals, and 489 of 1 779 teachers agreed to participate. Of teachers willing to participate in the follow-up, 52% were retained, three-quarters by post and a quarter by email. Their mean age was 46.3 years and 70.3% were female. The prevalence of CVD risk factors was high and featured hypertension (48.5%), hypercholesterolaemia (20.5%), smoking (18.0%), diabetes (10.1%) and chronic kidney disease (10.4%), while 84.7% were overweight or obese. Of the participants, 18.7% were at high risk of a heart attack or stroke within 10 years. CONCLUSION: Establishing a cohort study among teachers has challenges but also opportunities for addressing CVD, which will soon impose a substantial burden on Cape Town's education system.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".